Defect recognition system and defect recognition method

ABSTRACT

A defect recognition system and a defect recognition method are described. The method includes inspecting a wafer to generate a defect map and locating at least one defect on the wafer by using the defect map; analyzing light reflected from one of the at least one defect to obtain a spectrum of the light; comparing a waveform of the obtained spectrum with a plurality of waveforms respectively in spectrums of different substances; and determining a composition of the defect based on the comparison.

BACKGROUND

In the manufacturing processes of modem semiconductor devices, variousmaterials and machines are manipulated to create a final product.Manufacturers have dedicated to reduce particulate contamination duringprocessing so as to improve product yields. Due to the increasingcomplexity of semiconductor devices (e.g. many more layers andprocesses) and the development of larger wafers, the need for defectdetection and control is further emphasized.

The inspection on the semi-manufactured product is frequently performedduring manufacture by using wafer scanner in order to timely find thedefects. The wafer scanner may detect the defects, analyze the defectsto identify the types of the defects, and locate the defects on thewafer, so as to assist the operator in evaluating and correcting themanufacturing processes that cause the defects.

However, since there are hundreds of processes in manufacturing theproduct, the possible factors that cause the defects are considerableand the origin of the defect is hard to derive. As a result, theevaluation of defects highly relies on human knowledge and expertise andcosts a large amount of time and efforts, but the result is usually notsatisfactory.

BRIEF DESCRIPTION OF THE DRAWINGS

Aspects of the present disclosure are best understood from the followingdetailed description when read with the accompanying figures. It isnoted that, in accordance with the standard practice in the industry,various features are not drawn to scale. In fact, the dimensions of thevarious features may be arbitrarily increased or reduced for clarity ofdiscussion.

FIG. 1 is a schematic diagram illustrating a defect recognition systemin accordance with some embodiments of the present disclosure.

FIG. 2 is a flowchart illustrating a method of defect recognition inaccordance with some embodiments of the present disclosure.

FIG. 3 is a reflectivity spectrum of Aurum (Au) at any incidence anglefrom the wavelength distribution in accordance with some embodiments ofthe present disclosure.

FIG. 4 is a flowchart illustrating a method of defect recognition inaccordance with some embodiments of the present disclosure.

FIG. 5 is a diagram illustrating a characteristic curve of lightness inaccordance with some embodiments of the present disclosure.

FIG. 6 is a flowchart illustrating a method of defect recognition inaccordance with some embodiments of the present disclosure.

FIG. 7 is a diagram illustrating a characteristic curve of colorcomponent a* in accordance with some embodiments of the presentdisclosure.

DETAILED DESCRIPTION

The following disclosure provides many different embodiments, orexamples, for implementing different features of the provided subjectmatter. Specific examples of components and arrangements are describedbelow to simplify the present disclosure. These are, of course, merelyexamples and are not intended to be limiting. For example, the formationof a first feature over or on a second feature in the description thatfollows may include embodiments in which the first and second featuresare formed in direct contact, and may also include embodiments in whichadditional features may be formed between the first and second features,such that the first and second features may not be in direct contact. Inaddition, the present disclosure may repeat reference numerals and/orletters in the various examples. This repetition is for the purpose ofsimplicity and clarity and does not in itself dictate a relationshipbetween the various embodiments and/or configurations discussed.

Further, spatially relative terms, such as “beneath,” “below,” “lower,”“above,” “upper” and the like, may be used herein for ease ofdescription to describe one element or feature's relationship to anotherelement(s) or feature(s) as illustrated in the figures. The spatiallyrelative terms are intended to encompass different orientations of thedevice in use or operation in addition to the orientation depicted inthe figures. The apparatus may be otherwise oriented (rotated 90 degreesor at other orientations) and the spatially relative descriptors usedherein may likewise be interpreted accordingly.

In manufacturing the semiconductor product, an inspection is performedon a wafer to scan the surface of the wafer by using a light source(e.g. a laser) and measure the light reflected from the surface todetect potential defects. In the present disclosure, aforesaid reflectedlight is further directed to a spectrometer analyzer for analyzing thespectrum of the reflected light. Since the particulates that may beproduced or brought in during manufacturing processes are finite and allthe substances have their own unique spectrums, in the presentapplication, the spectrum obtained by analyzing the light reflected fromthe defect is compared with previously established spectrums of variouscompositions so as to derive the composition of the particulatesappeared on the defect. Based on the derived composition, an approximatetime of defect occurrence may be estimated by comparing the derivedcomposition with the substances involved in the manufacturing processesin a manufacturing schedule. As a result, the manufacturing process thatcauses the defect may be determined if the derived composition matchesone or more substances involved in the manufacturing process.

In view of the foregoing, the present disclosure provides a defectrecognition system. In some embodiments, the defect recognition systemincludes a light source, an inspecting component, a light analyzingcomponent and a processing component. The light source is configured toproject light on a wafer. The inspecting component is configured toinspect a wafer to generate a defect map and locate at least one defecton the wafer by using the defect map. The light analyzing component isconfigured to analyze the light reflected from one of the at least onedefect to obtain a spectrum of the light. The processing component isoperatively connected to the inspecting component and the lightanalyzing component, in which the processing component is programmed tocompare a waveform of the obtained spectrum with a plurality ofwaveforms respectively in the spectrums of different substances, anddetermine the composition of the defect based on the comparison.

FIG. 1 is a schematic diagram illustrating a defect recognition systemin accordance with some embodiments of the present disclosure. In someembodiments, the defect recognition system 10 includes a light source102, an inspecting component 104, a light analyzing component 106, aprocessing component 108, and optical components 110, 112, 114, 116, 118and 120.

In some embodiments, the light source 102 is a laser such as asemiconductor laser or a comb laser, or other light generators thatgenerate light with various wavelengths. The light source 102 projectslight on the wafer 12 such that the light reflected from the wafer 12can be detected by the inspecting component 104 and the light analyzingcomponent 106 for subsequent inspection and analysis.

In some embodiments, the inspecting component 104 includes aphotomultiplier detector or other light sensing elements such ascharge-coupled device (CCD), backside illumination (BSI) sensor, orcomplementary metal-oxide-semiconductor (CMOS) sensor, for detecting thelight reflected from the wafer 12 so as to inspect defects on the wafer12.

In some embodiments, the light analyzing component 106 is a spectrometeranalyzer that detects the light reflected from the wafer 12 and analyzesa spectrum of the detected light. In some embodiments, the lightanalyzing component 106 further calculates the light components of thereflected light based on the analyzed spectrum. In some embodiments, thelight components to be calculated include a lightness L*, a colorcomponent a*, or a color component b* in a Lab color space, or acombination thereof.

In some embodiments, the processing component 108 is a single-core or amulti-core central processing unit (CPU) or any other programmablegeneral purpose or specific purpose microprocessor, digital signalprocessor (DSP), programmable controller, application specificintegrated circuits (ASIC), programmable logic device (PLD), othersimilar devices, or a combination of these devices. The processingcomponent 108 is operatively connected to the inspecting component 104and the light analyzing component 106. In some embodiments, theprocessing component 108 is configured to access and execute theprograms recorded in a computer-readable storage medium (not shown), soas to implement a method of defect recognition in accordance with someembodiments of the present disclosure.

As shown in FIG. 1, the light source 102 produces polarized light atdiscrete wavelengths. The light passes through a filter 110 whichisolates the beam of light. The filter 110 may further include opticswhich expand the beam and then focus the beam on a pinhole aperture. Thediameter of the pinhole aperture is selected to allow easy alignment ofthe beam of light. The light having the selected wavelength(s) passesfrom the filter 110 to a beam splitter 112 which reflects the light ofthe proper polarization and directs the light to an objective lens 114.The objective lens 114 demagnifies the light and projects the light onthe wafer 12. The light is focused in the focal plane of the objectivelens 114.

According to the principles of confocal imaging, the light striking thewafer 12 is scattered and a portion of the light is reflected back tothe objective lens 114 and returned through the optical path describedabove. The returning light passes through the beam splitters 112 and 116and reaches the filter 118 with a pinhole aperture. The light whichpasses through the aperture is imaged on the inspecting component 104which generates a defect map of the wafer 12. The inspecting component104 further locates the defects on the wafer 12 by using the defect map.On the other hand, as the returning light passes through the beamsplitter 116, a portion of light is directed to the filter 120 with apinhole aperture. The light which passes through the aperture isdetected by the light analyzing component 106, which then analyzes thelight reflected from the defect to obtain a spectrum of the light. Thedata processed by the inspecting component 104 and the light analyzingcomponent 106 is then transmitted to the processing component 108 forfurther processing.

In some embodiments, the defect recognition system 10 is adapted forcarrying out a method of defect recognition in accordance with someembodiments of the present disclosure. In detail, FIG. 2 is a flowchartillustrating a method of defect recognition in accordance with someembodiments of the present disclosure.

In step 202, the inspecting component 104 inspects the wafer 12 togenerate a defect map and locates the defects on the wafer 12 by usingthe defect map. In some embodiments, the inspecting component 104 scansthe wafer 12 by lines to generate data of lines representing lightreflected from the lines of the wafer 12, compares a plurality offeatures of a sample image formed by the data of lines with acorresponding plurality of features of a reference image, and recognizesfeatures in the sample image deviating from corresponding features ofthe reference image as the defect based on the comparison. In someembodiments, the defect recognition system 10 further includes acomputer-readable storage medium (not shown) operably connected to theinspecting component 104 for storing the reference image.

In step 204, the light analyzing component 106 analyzes the lightreflected from one of the at least one defect to obtain a spectrum ofthe light. In step 206, the processing component 108 compares a waveformof the obtained spectrum with a plurality of waveforms respectively inspectrums of different substances, such as Wolfram (W), Aurum (Au),Copper (Cu), Titanium (Ti), Tantalum (Ta), or Carbon (C), or any otherelement or chemical composition. In step 208, the processing component108 determines the composition of the defect based on the comparison instep 206.

In some embodiments, aforesaid spectrums of substances are previouslyestablished by the processing component 108 by using historical data ofanalysis and stored in a spectrum database in a computer-readablestorage medium (not shown) operably connected to the processingcomponent 108. In detail, the processing component 108 determines aplurality of compositions that are probably involved in themanufacturing processes of the wafer 12, and respectively disposes thesubstances on the wafer 12 by using injecting or implanting tools. Then,the processing component 108 controls the light source 102 to projectlight on the disposing area of the wafer 12, and activates the lightanalyzing component 106 to analyze the light reflected from thedisposing area, so as to obtain the spectrums of substances.

For example, FIG. 3 is a reflectivity spectrum of Aurum (Au) at anyincidence angle from the wavelength distribution in accordance with someembodiments of the present disclosure. Referring to FIG. 3, a waveform30 of an Au reflectivity spectrum shows the relation between the lightreflectances and the light wavelengths of the light reflected from thedisposing area of Au on the wafer. The waveform 30 is compared with thewaveform of the spectrum currently detected and once the spectrum ofAurum is matched with the current spectrum, the composition of theparticulates on the wafer is determined to contain Au.

It is noted that, based on the determined composition of the defect, theprocessing component 108 may further derive the occurrence time of thedefect. In detail, in some embodiments, the processing component 108inquires substances, such as Wolfram (W), Copper (Cu), or Carbon (C),which are respectively involved in the manufacturing processes in amanufacturing schedule with the composition of the defect, so as todetermine the manufacturing process that causes the defect. For example,if the composition of the defect is determined to contain Carbon, it maybe further determined that the suspect tool causing the defect is thewafer box and the suspect manufacturing processes are the manufacturingprocesses that use the wafer box since the wafer box is the toolcomprising Carbon.

Based on the above, the composition of the defect can be derived, andthe manufacturing process or the manufacturing tool that causes thedefect can be determined, so as to enhance the capability of defectrecognition, reduce the cycle time, and improve the business impact.

It is noted that, in some embodiments, after obtaining the spectrum ofthe reflected light, the processing component 108 may further calculatethe light components of the reflected light based on the obtainedspectrum and accordingly estimate occurrence time of the defect. To bespecific, through cross-comparison between the composition and theoccurrence time of the defect, a more accurate occurrence time of thedefect or manufacturing process that causes the defect can be derived.

In detail, FIG. 4 is a flowchart illustrating a method of defectrecognition in accordance with some embodiments of the presentdisclosure. Referring to FIG. 4, in step 402, the inspecting component104 inspects the wafer 12 to generate a defect map and locates thedefects on the wafer 12 by using the defect map. In step 404, the lightanalyzing component 106 analyzes the light reflected from one of the atleast one defect to obtain a spectrum of the light. In step 406, theprocessing component 108 compares a waveform of the obtained spectrumwith a plurality of waveforms respectively in spectrums of differentsubstances. In step 408, the processing component 108 determines thecomposition of the defect based on the comparison in step 406. Aforesaidsteps 402 to 408 are the same as or similar to steps 202 to 208 in theprevious embodiments, and therefore the detailed description is notrepeated herein.

The difference between the present embodiments and the previousembodiments lies in that, in step 410, the processing component 108further calculates at least one light component of the light reflectedfrom the defect based on the obtained spectrum of the light. In someembodiments, the light components to be calculated include a lightnessL*, a color component a*, or a color component b* in a Lab color space,or a combination thereof.

In step 412, the processing component 108 compares one of the at leastone light component with a characteristic curve of the correspondinglight component of the defect. In step 414, the processing component 108estimates an occurrence time of the defect based on the comparison instep 206. In detail, in some embodiments, the processing component 108inquires a characteristic curve of one of the light components and looksup the currently measured light component in the characteristic curve tofind a corresponding time period. The characteristic curve records decayof the light component along with time and the time period being foundrepresents how long it takes for the light component to decay from thelight component calculated when the defect just occurs to the currentlycalculated light component. Therefore, the processing component 108 mayobtain the occurrence time of the defect by subtracting the time periodfrom the inspection time of analyzing the reflected light.

In some embodiments, aforesaid characteristic curves of the lightcomponents are previously established by the processing component 108 byusing historical data of measurements and stored in a computer-readablestorage medium (not shown) operably connected to the processingcomponent 108. In detail, taking lightness for example, the processingcomponent 108 collects a plurality of measurements of lightness of thelight reflected from the defect measured at different time points andcollects the time for performing the measurements. Then, the processingcomponent 108 labels the measurements in a xy-coordinate plane,calculates a trend line of the measurements by using a regressionanalysis method and uses the trend line as the characteristic curve oflightness. In some embodiments, the values of measurements may befurther processed to enhance the variance of the measurements along withtime such as calculating contrast values of the measurements orcalculating natural log values of the measurements or the contrastvalues.

In step 416, the processing component 108 inquires substancesrespectively involved in a plurality of manufacturing processes in amanufacturing schedule with the composition of the defect and inquiresthe manufacturing schedule with the occurrence time of the defect, so asto determine the manufacturing process that causes the defect.

In detail, since the manufacturing schedule records the substances usedin the manufacturing processes, as the composition of the defect isdetermined, it is easy to find the manufacturing process that causes thedefect by comparing the composition with the substances involved in themanufacturing process. On the other hand, since the manufacturingschedule also records the time periods of the manufacturing processes,as the occurrence time (or occurrence date) of the defect is estimated,it is easy to find the manufacturing process that causes the defect bycomparing the estimated occurrence time with the time periods of themanufacturing processes in the manufacturing schedule. As a result,through cross-comparison between the composition and the occurrence timeof the defect, a more accurate determination on the manufacturingprocess that causes the defect can be obtained.

It is noted that, in some embodiments, the processing component 108estimates the occurrence time of the defect based on the lightness.Accordingly, the processing component 108 inquires a characteristiccurve of the lightness, and then looks up the currently measuredlightness in the characteristic curve to find a corresponding timeperiod. In some embodiments, the processing component 108 calculates anatural log value of a contrast value of lightness so as compare thenatural log value with the characteristic curve of the lightness to findthe corresponding time period. Finally, the processing component 108subtracts the time period from the inspection time of measuring thelightness and obtains the occurrence time of the defect.

For example, FIG. 5 is a diagram illustrating a characteristic curve oflightness in accordance with some embodiments of the present disclosure.

In FIG. 5, the horizontal axis represents the time of performing themeasurement and the vertical axis represents a contrast value oflightness (i.e. L* value) being measured. The sampling points representcontrast values of the measurements of lightness detected at differenttime points. The trend line 500 of the sampling points is calculatedbased on the coordinates of the sampling points by using a regressionanalysis method. Accordingly, when a current lightness of the lightreflected from the defect is measured, the contrast value of thelightness between the lightness with respect to the defect and thelightness with respect to non-defective device in the wafer iscalculated and the trend line 500 is looked up by the contrast value soas to find the time period it takes for the lightness to decay. Forexample, if a contrast value of the currently measured lightness isequal to 55, it is found the corresponding time is about 65 daysaccording to the trend line 500. Therefore, by tracking back 65 daysfrom the day of performing the measurement, the occurrence time of thedefect is obtained.

In some embodiments, the processing component 108 estimates theoccurrence time of the defect based on the types of the lightcomponents. Accordingly, the processing component 108 inquires thecharacteristic curves of the light components, compares the lightcomponents measured from the light reflected from the located defectwith the characteristic curves of the corresponding light components ofthe defect to find corresponding time periods. The processing component108 respectively subtracts the time periods from the inspection time ofanalyzing the reflected light to derive the occurrence time of thedefect. In some embodiments, the processing component 108 calculatesstatistics of the occurrence time estimated based on the lightcomponents to determine a final result of the occurrence time, in whichthe statistics is a mean, a median, a standard deviation, a confidenceinterval (CI), or percentiles.

In some embodiments, the processing component 108 estimates theoccurrence time mainly based on measurements of lightness L* andmodifies the occurrence time based on measurements of other lightcomponents such as color components a* and b*. For example, FIG. 6 is aflowchart illustrating a method of defect recognition in accordance withsome embodiments of the present disclosure.

Referring to FIG. 1 and FIG. 6, in step 602, the processing component108 compares the measured lightness L* with a characteristic curve ofthe lightness L* of the defect. In step 604, the processing component108 estimates the occurrence time based on the comparison of lightnessL*. In step 606, the processing component 108 compares the measuredcolor component a* with a characteristic curve of the color component a*of the defect and compares the measured color component b* with acharacteristic curve of the color component b* of the defect. In step608, the processing component 108 modifies the estimated occurrence timebased on the comparison of the color components a* and b*.

In some embodiments, the processing component 108 modifies the estimatedoccurrence time by multiplying the time periods derived based on thelightness and the color components a* and b* with different weights. Forexample, FIG. 7 is a diagram illustrating a characteristic curve ofcolor component a* in accordance with some embodiments of the presentdisclosure.

In FIG. 7, the horizontal axis represents the time of performing themeasurement and the vertical axis represents a natural log value of acontrast value of color component a* (i.e. a* value) in the Lab colorspace. The sampling points represent the natural log values of contrastvalues of the measurements of color component a* detected atcorresponding time points. The trend line 700 of the sampling points iscalculated based on the coordinates of the sampling points by using aregression analysis method. Accordingly, when a current color componenta* of the light reflected from the defect is measured, a natural logvalue of the contrast value of the color component a* between the colorcomponent a* with respect to the defect and the color component a* withrespect to non-defective device in the wafer is calculated and the trendline 700 is looked up by using the natural log value so as to find thetime period it takes for the color component a* to decay. For example,if an a* value of the currently measured color component a* is equal to53, it is found the corresponding time is about 70 days according to thetrend line 700.

In some embodiments, the time period (i.e. 65 days) derived based on thelightness and the time period (i.e. 70 days) derived based on the colorcomponent a* are multiplied with weights 80% and 20%, respectively, anda sum of the products (i.e. 65*80%+70*20%=66 days) is calculated andused as a final result of the time period. Finally, the time period issubtracted from the inspection time to obtain the occurrence time of thedefect.

In some embodiments, aforesaid weights of light components aredetermined based on certain criterions. In some embodiments, acoefficient of determination (i.e. R-squared) indicating how well thedata of sampling points fit the calculated trend line is calculated andused as a reference to determine the weights of light components. Forexample, referring to the embodiments of FIG. 7, a R-squared withrespect to color component a* is calculated based on the a* values andthe trend line 700 in FIG. 7. If the calculated R-squared is larger thanor equal to 0.9, the weight of the color component a* is set as 0.2while the weight of lightness is set as 0.8. If the calculated R-squaredis between 0.7 to 0.9, the weight of the color component a* is set as0.7 while the weight of lightness is set as 0.3. If the calculatedR-squared is less than 0.7, the weight of the color component a* is setas 0, which means the color component a* is not used as a reference tomodify the occurrence time estimated based on the comparison oflightness L*.

In some embodiments, the processing component 108 estimates theoccurrence time based on multiple defects. In detail, since the defectsmay appear in group due to same process or same stage, a statisticalmethod is further applied to the occurrence time derived from multipledefects so as to converge the occurrence time of the defects. As aresult, a more accurate occurrence time of the defects is obtained.

In some embodiments, the processing component 108 estimates occurrencetime of the defects in the defect map by using the method illustrated inthe embodiments of FIG. 4. The processing component 108 sorts theestimated occurrence time of the defects in the defect map, and thencalculates an interval of a median of the estimated occurrence time witha deviation so as to determine a final result of the occurrence time. Insome embodiments, the deviation is equal to a product of a factor and adifference of a 97th percentile and the median of the estimatedoccurrence time. In some embodiments, the factor is ranging from 1 to k,in which k is an arbitrary number larger than 1 and is determined basedon the selection of confidence level. In some embodiments, the factor isequal to 19.6 which is the approximate value of the 97.5 percentilepoint of the normal distribution and used in the construction ofapproximate 95% confidence intervals, in which 95% of the area under anormal curve lies within roughly 1.96 standard deviations of the mean.In some embodiments, the factor is equal to a product or a quotient of1.96 and another experiential or experimental value.

For example, in some embodiments, the processing component 108calculates an interval T of the occurrence time of defects throughfollowing equation:

T=P ₅₀±1.96×(P ₉₇ −P ₅₀)÷1.88

In detail, the interval T is equal to a 50th percentile P₅₀ (i.e. amedian) of the estimated occurrence time derived based on the defectswith a deviation, and the deviation is equal to a product of a factorand a difference between a 97th percentile P₉₇ and a 50th percentile P₅₀of the estimated occurrence time derived based on the defects. Thefactor is equal to a quotient of 1.96 divided by 1.88 which isdetermined based on experimental results.

In some embodiments, a non-transitory computer-readable mediumcomprising processor executable instructions that when executed performa method for recognizing defects on a wafer as illustrated in theembodiments above. In some embodiments, the non-transitorycomputer-readable medium is a CD-R, a DVD-R, a flash drive, or a platterof a hard disk drive, etc., on which is encoded computer-readable data.The computer-readable data, such as binary data comprising a pluralityof zeros and ones, in turn comprises a set of computer instructionsconfigured to operate according to one or more of the principles setforth herein. In some embodiments, the processor-executable computerinstructions are configured to perform a method for recognizing defects,such as at least some of the exemplary method illustrated in FIG. 2, forexample. Many such computer-readable media are devised by those ofordinary skill in the art that are configured to operate in accordancewith the techniques presented herein.

According to some embodiments, a defect recognition system includes alight source configured to project light on a wafer, an inspectingcomponent configured to inspect the wafer to generate a defect map andlocate at least one defect on the wafer by using the defect map, a lightanalyzing component configured to analyze the light reflected from oneof the at least one defect, a processing component operatively connectedto the inspecting component and the light analyzing component. Theprocessing component is programmed to compare a waveform of the obtainedspectrum with a plurality of waveforms respectively in spectrums ofdifferent substances, and determine a composition of the defect based onthe comparison.

According to some embodiments, a defect recognition method includesinspecting a wafer to generate a defect map and locating at least onedefect on the wafer by using the defect map, analyzing light reflectedfrom one of the at least one defect to obtain a spectrum of the light,comparing a waveform of the obtained spectrum with a plurality ofwaveforms respectively in spectrums of different substances, anddetermining a composition of the defect based on the comparison.

According to some embodiments, a non-transitory computer-readable mediumincludes processor executable instructions that when executed perform amethod for recognizing at least one defect on a wafer. The methodincludes inspecting the wafer to generate a defect map and locating theat least one defect in the defect map, analyzing light reflected fromone of the at least one defect to obtain a spectrum of the light,comparing a waveform of the obtained spectrum with a plurality ofwaveforms respectively in spectrums of different substances, anddetermining a composition of the defect based on the comparison.

The foregoing outlines features of several embodiments so that thoseskilled in the art may better understand the aspects of the presentdisclosure. Those skilled in the art should appreciate that they mayreadily use the present disclosure as a basis for designing or modifyingother processes and structures for carrying out the same purposes and/orachieving the same advantages of the embodiments introduced herein.Those skilled in the art should also realize that such equivalentconstructions do not depart from the spirit and scope of the presentdisclosure, and that they may make various changes, substitutions, andalterations herein without departing from the spirit and scope of thepresent disclosure.

1. A defect recognition system, comprising: a light source configured toproject light on a wafer; a light detector configured to inspect thewafer to generate a defect map and locate at least one defect on thewafer by using the defect map; a spectrum analyzer configured to analyzethe light reflected from one of the at least one defect to obtain aspectrum of the light; and a processor operatively connected to thelight detector and the spectrum analyzer, wherein the processor isprogrammed to compare a waveform of the obtained spectrum with aplurality of waveforms respectively in spectrums of different substancesto determine a composition of the defect, wherein the light detectorscans the wafer by lines to generate data of lines representing lightreflected from the lines of the wafer, compares a plurality of featuresof a sample image formed by the data of lines with a correspondingplurality of features of a reference image, and recognizes features inthe sample image deviating from corresponding features of the referenceimage as the defect based on the comparison.
 2. (canceled)
 3. The defectrecognition system of claim 1, further comprising a computer-readablestorage medium operably connected to the light detector, wherein thestorage medium stores the reference image.
 4. The defect recognitionsystem of claim 1, further comprising a computer-readable storage mediumoperably connected to the processor, wherein the storage medium stores aspectrum database comprising the spectrums of different substances. 5.The defect recognition system of claim 1, wherein the processor furthercomprises circuitry for inquiring substances respectively involved in aplurality of manufacturing processes in a manufacturing schedule withthe composition of the defect to determine the manufacturing processthat causes the defect.
 6. The defect recognition system of claim 1,wherein the processor further comprises circuitry for calculating atleast one light component of light reflected from the defect from theobtained spectrum of the light, and comparing the at least one lightcomponent with a characteristic curve of the corresponding lightcomponent of the defect to estimate an occurrence time of the defect. 7.The defect recognition system of claim 6, wherein the processor furthercomprises circuitry for subtracting a time period corresponding to thecalculated light component in the characteristic curve from aninspection time of analyzing the light reflected from the defect toobtain the occurrence time of the defect.
 8. The defect recognitionsystem of claim 6, wherein the processor further comprises circuitry forinquiring substances respectively involved in a plurality ofmanufacturing processes in a manufacturing schedule with the compositionof the defect and inquiring the manufacturing schedule with theoccurrence time of the defect to determine the manufacturing processthat causes the defect.
 9. The defect recognition system of claim 6,wherein the at least one light component comprises a lightness L*, acolor component a*, or a color component b* in a Lab color space, or acombination thereof.
 10. The defect recognition system of claim 6,further comprising a computer-readable storage medium operably connectedto the processor, wherein the storage medium stores the characteristiccurve of the light component of the at least one defect.
 11. A defectrecognition method, comprising: inspecting a wafer to generate a defectmap and locating at least one defect on the wafer by using the defectmap; analyzing light reflected from one of the at least one defect toobtain a spectrum of the light; comparing a waveform of the obtainedspectrum with a plurality of waveforms respectively in spectrums ofdifferent substances; and determining a composition of the defect basedon the comparison, wherein the step of inspecting the wafer to generatethe defect map and locating the at least one defect in the defect mapcomprises: scanning the wafer by lines to generate data of linesrepresenting light reflected from the lines of the wafer; comparing aplurality of features of a sample image formed by the data of lines witha corresponding plurality of features of a reference image; andrecognizing features in the sample image deviating from correspondingfeatures of the reference image as the defect based on the comparison.12. (canceled)
 13. The method of claim 11, wherein after the step ofdetermining the composition of the defect based on the comparison, themethod further comprises: inquiring substances respectively involved ina plurality of manufacturing processes in a manufacturing schedule withthe composition of the defect to determine the manufacturing processthat causes the defect.
 14. The method of claim 11, wherein before thestep of comparing the waveform of the obtained spectrum with theplurality of waveforms respectively in the spectrums of differentsubstances, the method further comprises: determining a plurality ofcompositions involved in a plurality of manufacturing processes; andrespectively disposing the compositions on the wafer, projecting thelight on a disposing area of the wafer, and analyzing the lightreflected from the disposing area to obtain the spectrums of differentsubstances.
 15. The method of claim 11, wherein after the step ofanalyzing light reflected from one of the at least one defect to obtainthe spectrum of the light, the method further comprises: calculating atleast one light component of light reflected from the defect from theobtained spectrum of the light, and comparing the at least one lightcomponent with a characteristic curve of the corresponding lightcomponent of the defect to estimate an occurrence time of the defect.16. The method of claim 15, wherein the step of estimate the occurrencetime of the defect based on the comparison comprises: subtracting a timeperiod corresponding to the measured light component in thecharacteristic curve from an inspection time of measuring the lightcomponent of the defect to obtain the occurrence time of the defect. 17.The method of claim 15, further comprising: inquiring substancesrespectively involved in a plurality of manufacturing processes in amanufacturing schedule with the composition of the defect and inquiringthe manufacturing schedule with the occurrence time of the defect todetermine the manufacturing process that causes the defect.
 18. Themethod of claim 15, wherein the at least one light component comprises alightness L*, a color component a*, or a color component b* in a Labcolor space, or a combination thereof.
 19. The method of claim 15,wherein before the step of comparing the at least one light componentmeasured from the light reflected from the located defect with thecharacteristic curve of the corresponding light component of the defect,the method further comprises: collecting a plurality of measurements ofone of the at least one light component of the light reflected from thedefect and time for performing the measurements; and calculating a trendline of the measurements of the light component and time for performingthe measurements as the characteristic curve of the light component. 20.A non-transitory computer-readable medium comprising processorexecutable instructions that when executed perform a method forrecognizing at least one defect on a wafer, the method comprising:inspecting a wafer to generate a defect map and locating at least onedefect on the wafer by using the defect map; analyzing light reflectedfrom one of the at least one defect to obtain a spectrum of the light;comparing a waveform of the obtained spectrum with a plurality ofwaveforms respectively in spectrums of different substances; anddetermining a composition of the defect based on the comparison, whereinthe step of inspecting the wafer to generate the defect map and locatingthe at least one defect in the defect map comprises: scanning the waferby lines to generate data of lines representing light reflected from thelines of the wafer; comparing a plurality of features of a sample imageformed by the data of lines with a corresponding plurality of featuresof a reference image; and recognizing features in the sample imagedeviating from corresponding features of the reference image as thedefect based on the comparison.